Development of Classification Framework Using Machine Learning and Pattern Recognition System

Performance of modern automated pattern recognition (PR) systems is heavily influenced by accuracy of their feature extraction algorithm. Many papers have demonstrated uses of deep learning techniques in PR, but there is little evidence on using them as feature extractors. Our goal is to contribute to this field and perform a comparative study between classical used methods in feature extraction and deep learning techniques. For that, a biometric recognition system, which is a PR application, is developed and evaluated using a proposed evaluation metric called expected risk probability. In our study, two deeply learned features, based on PCANet and DCTNet deep learning techniques, are used with two biometric modalities that are palmprint and palm-vein. Subsequently, the efficiency of these techniques is compared with various classical feature extraction methods. From the obtained results, we drew our conclusions on a very positive impact of deep learning techniques on overall recognition rate, and thus these techniques significantly outperform the classical techniques.

Purpose:This study aims to compare the accuracy of the ADNEX MR scoring system and pattern recognition system to evaluate adnexal lesions indeterminate on the US exam. Methods:In this cross-sectional retrospective study, pelvic DCE-MRI of 245 patients with 340 adnexal masses was studied based on the ADNEX MR scoring system and pattern recognition system. Results:ADNEX MR scoring system with a sensitivity of 96.6% and specificity of 91% has an accuracy of 92.9%. The pattern recognition system’s sensitivity, specificity, and accuracy are 95.8%, 93.3%, and 94.7%, respectively. PPV and NPV for the ADNEX MR scoring system were 85.1 and 98.1, respectively. PPV and NPV for the pattern recognition system were 89.7% and 97.7%, respectively. The area under the ROC curve for the ADNEX MR scoring system and pattern recognition system is 0.938 (95% CI, 0.909-0.967) and 0.950 (95% CI, 0.922-0.977). Pairwise comparison of these AUCs showed no significant difference (p = 0.052). Conclusion:The pattern recognition system is less sensitive than the ADNEX MR scoring system, yet more specific.

Due to the development of technology and computer science, especially in the field of artificial intelligence and machine learning, today information technologies have become the most important element in most fields. The developments in these technologies included building models and systems that depend mainly on machine learning. One of the most important areas that we need to develop is pattern recognition and classification. The term patterns include giving the computer the ability to recognize and classify images, shapes, sounds, and digital and textual data into different classes. These systems depend on building a model based on artificial intelligence and a trained neural network to analyze and classify patterns. In this article, we develop neural network algorithms based on five different datasets to build an intelligent pattern classification and recognition system by providing a neural network method for pattern recognition (NNM - PR). Using these algorithms, a high accuracy scale was obtained compared to previous studies.

Fatal accidents are a major issue hindering the wide acceptance of safety-critical systems that employ machine learning and deep learning models, such as automated driving vehicles. In order to use machine learning in a safety-critical system, it is necessary to demonstrate the safety and security of the system through engineering processes. However, thus far, no such widely accepted engineering concepts or frameworks have been established for these systems. The key to using a machine learning model in a deductively engineered system is decomposing the data-driven training of machine learning models into requirement, design, and verification, particularly for machine learning models used in safety-critical systems. Simultaneously, open problems and relevant technical fields are not organized in a manner that enables researchers to select a theme and work on it. In this study, we identify, classify, and explore the open problems in engineering (safety-critical) machine learning systems—that is, in terms of requirement, design, and verification of machine learning models and systems—as well as discuss related works and research directions, using automated driving vehicles as an example. Our results show that machine learning models are characterized by a lack of requirements specification, lack of design specification, lack of interpretability, and lack of robustness. We also perform a gap analysis on a conventional system quality standard SQuaRE with the characteristics of machine learning models to study quality models for machine learning systems. We find that a lack of requirements specification and lack of robustness have the greatest impact on conventional quality models.

Strategic Environmental Assessment (SEA) Process towards the Sustainable Design and Construction of Computer, Communication, Network Engineering, Machine Learning and Artificial Intelligence Systems

Reducing the effect of wrist variation on pattern recognition of Myoelectric Hand Prostheses Control through Dynamic Time Warping

The research aims to identify a suitable machine learning model between various machine learning (ML) systems analysed. The input data are factors identified through principal component analysis (PCA) of potential entrepreneurs in the B40 category by analysing 1000 responses from this group through a survey instrument. The following machine learning systems are tested; Random Forest, Extra Trees, K-Neighbots, SVC, Ridge Classifier, Logistic Regression and Decision Tree to select and identify a case-based reasoning artificial intelligence (AI) system best suited in this scenario. Given the data set size, results based on accuracy indicate the best algorithm is Logistic Regression.

As automated machine learning (AutoML) systems continue to progress in both sophistication and performance, it becomes important to understand the ‘how’ and ‘why’ of human-computer interaction (HCI) within these frameworks, both current and expected. Such a discussion is necessary for optimal system design, leveraging advanced data-processing capabilities to support decision-making involving humans, but it is also key to identifying the opportunities and risks presented by ever-increasing levels of machine autonomy. Within this context, we focus on the following questions: (i) What does HCI currently look like for state-of-the-art AutoML algorithms, especially during the stages of development, deployment, and maintenance? (ii) Do the expectations of HCI within AutoML frameworks vary for different types of users and stakeholders? (iii) How can HCI be managed so that AutoML solutions acquire human trust and broad acceptance? (iv) As AutoML systems become more autonomous and capable of learning from complex open-ended environments, will the fundamental nature of HCI evolve? To consider these questions, we project existing literature in HCI into the space of AutoML; this connection has, to date, largely been unexplored. In so doing, we review topics including user-interface design, human-bias mitigation, and trust in artificial intelligence (AI). Additionally, to rigorously gauge the future of HCI, we contemplate how AutoML may manifest in effectively open-ended environments. This discussion necessarily reviews projected developmental pathways for AutoML, such as the incorporation of high-level reasoning, although the focus remains on how and why HCI may occur in such a framework rather than on any implementational details. Ultimately, this review serves to identify key research directions aimed at better facilitating the roles and modes of human interactions with both current and future AutoML systems.

INTRODUCTIONMachine learning is increasingly utilized over recent years in order to develop models that represent and solve problems in a variety of domains, including those of obstetrics and midwifery. The aim of this systematic review was to analyze research studies on machine learning and intelligent systems applications in midwifery and obstetrics.METHODSA thorough literature review was performed in four electronic databases (PubMed, APA PsycINFO, SCOPUS, ScienceDirect). Only articles that discussed machine learning and intelligent systems applications in midwifery and obstetrics, were considered in this review. Selected articles were critically evaluated as for their relevance and a contextual synthesis was conducted.RESULTSThirty-two articles were included in this systematic review as they met the inclusion and methodological criteria specified in this study. The results suggest that machine learning and intelligent systems have produced successful models and systems in a broad list of midwifery and obstetrics topics, such as diagnosis, pregnancy risk assessment, fetal monitoring, bladder tumor, etc.CONCLUSIONSThis systematic review suggests that machine learning represents a very promising area of artificial intelligence for the development of practical and highly effective applications that can support human experts, as well the investigation of a wide range of exciting opportunities for further research.

The authors demonstrate how a pattern-recognition system can be applied to the interpretation of capacitance-voltage (C-V) curves on an MOS test structure. By intelligently sequencing additional measurements it is possible to accurately extract the maximum amount of information available from C-V and conductance-voltage (G-V) measurements. The expert system described, (CV-EXPERT), is completely integrated with the measurement, instrumentation, and control software and is thus able to call up a sequence of individually tailored tests for the MOS test structure under investigation. The prototype system is able to correctly identify a number of process faults, including a leaky oxide, as shown. Improvements that could be gained from developing rules to coordinate G-V, capacitance-time, and doping profile measurements simply by recognizing the important factors in the initial C-V measurement are illustrated. >

An analysis of agricultural resource management and the optimization of systems through the Intergration of advanced algorithms primarily revolves around the examination and the implementation of efficient innovative and computational techniques intended at enhancing agricultural productivity. Given this, the integration and use of algorithms that span from genetic algorithms, machine learning, and other artificial intelligence systems and methods that provide room for pest control, resource allocation, and the element of yield prediction. Advanced algorithms play a fundamental role in the facilitation of real-time data processing as well as decision making driven towards enabling farmers to focus on the optimization of fertilization, water usage, and crop rotation processes and schedules. Machine learning systems and models are further trained on real-time and historical data to predict pest infestations, weather patterns, and crop diseases-thus providing a proactive approach to the minimization of losses. On the other hand, genetic algorithms are further employed in the development of optimal planting methods while improving crop varieties, hence ensuring higher productivity and resilience. Further, these systems widely contribute to the process of sustainable farming procedures through the minimization of wastage and the promotion of eco-friendly agricultural practices. Through the analysis of weather conditions, soil health, and crop requirements, the designed algorithms ensure proper use of resources, resulting in increased profitability and cost saving practices for farmers. This article therefore seeks to conduct an analysis on agricultural resource management and optimization systems through the integration of advanced algorithms.

The suitability of artificial immune systems for recognizing visual patterns is discussed. A new algorithm and software implementation of an artificial immune system have been proposed based on which real-time pattern recognition can be done using a Web camera. It has been shown experimentally that this system can be successfully used to recognize both human faces and any other objects. An issue of using an artificial immune system in high-performance parallel computing systems is discussed. The advantages of the developed artificial immune system include the ability to teach the system a new image in a fast manner at any moment during run-time. These advantages open up a possibility of creating artificial intelligence systems for real-time learning.

Bio-Inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition comprises papers on diverse aspects of bio-inspired models, soft computing and hybrid intelligent systems. The articles are divided into four main parts. The first one consists of papers that propose new fuzzy and bio-inspired models to solve general problems. The second part deals with the main theme of modular neural networks in pattern recognition, which are basically papers using bio-inspired techniques. The third part contains papers that apply hybrid intelligent systems to the problem of time series analysis and prediction, while the fourth one shows papers dealing with bio-inspired models in optimization and robotics applications. An edited book in which both theoretical and application aspects are covered.

We introduce a system for machine recognition of music patterns. The problem is put into a pattern recognition framework in the sense that an error between a target pattern and scanned pattern is minimized. The error takes into account pitch and rhythm information. The pitch error measure consists of an absolute (objective) error and a perceptual error. The latter depends on an algorithm for establishing the tonal context which is based on Krumhansl's (1990) key-finding algorithm. The sequence of maximum correlations that it outputs is smoothed with a cubic spline and is used to determine weights for perceptual and absolute pitch errors. Maximum correlations are used to create the assigned key sequence, which is then filtered by a recursive median filter to improve the structure of the output of the key finding algorithm. A procedure for choosing weights given to pitch and rhythm errors is discussed.

Limitations of the chest X-ray (CXR) have resulted in attempts to create machine learning systems to assist clinicians and improve interpretation accuracy. An understanding of the capabilities and limitations of modern machine learning systems is necessary for clinicians as these tools begin to permeate practice. This systematic review aimed to provide an overview of machine learning applications designed to facilitate CXR interpretation. A systematic search strategy was executed to identify research into machine learning algorithms capable of detecting >2 radiographic findings on CXRs published between January 2020 and September 2022. Model details and study characteristics, including risk of bias and quality, were summarized. Initially, 2248 articles were retrieved, with 46 included in the final review. Published models demonstrated strong standalone performance and were typically as accurate, or more accurate, than radiologists or non-radiologist clinicians. Multiple studies demonstrated an improvement in the clinical finding classification performance of clinicians when models acted as a diagnostic assistance device. Device performance was compared with that of clinicians in 30% of studies, while effects on clinical perception and diagnosis were evaluated in 19%. Only one study was prospectively run. On average, 128,662 images were used to train and validate models. Most classified less than eight clinical findings, while the three most comprehensive models classified 54, 72, and 124 findings. This review suggests that machine learning devices designed to facilitate CXR interpretation perform strongly, improve the detection performance of clinicians, and improve the efficiency of radiology workflow. Several limitations were identified, and clinician involvement and expertise will be key to driving the safe implementation of quality CXR machine learning systems.